Overview

Dataset statistics

Number of variables20
Number of observations4430
Missing cells0
Missing cells (%)0.0%
Duplicate rows1
Duplicate rows (%)< 0.1%
Total size in memory726.8 KiB
Average record size in memory168.0 B

Variable types

Numeric8
Categorical12

Alerts

Dataset has 1 (< 0.1%) duplicate rowsDuplicates
age is highly overall correlated with age_rangeHigh correlation
height is highly overall correlated with weight and 2 other fieldsHigh correlation
weight is highly overall correlated with height and 9 other fieldsHigh correlation
waist_circum_preferred is highly overall correlated with weight and 10 other fieldsHigh correlation
hip_circum is highly overall correlated with weight and 7 other fieldsHigh correlation
bmi is highly overall correlated with weight and 9 other fieldsHigh correlation
rcc is highly overall correlated with weight and 4 other fieldsHigh correlation
ict is highly overall correlated with weight and 11 other fieldsHigh correlation
age_range is highly overall correlated with ageHigh correlation
gender is highly overall correlated with height and 2 other fieldsHigh correlation
gender_bin is highly overall correlated with height and 2 other fieldsHigh correlation
obesity_bmi is highly overall correlated with weight and 10 other fieldsHigh correlation
obesity_bmi_txt is highly overall correlated with weight and 10 other fieldsHigh correlation
obesity_cc is highly overall correlated with weight and 12 other fieldsHigh correlation
obesity_cc_txt is highly overall correlated with weight and 12 other fieldsHigh correlation
obesity_rcc is highly overall correlated with obesity_cc and 3 other fieldsHigh correlation
obesity_rcc_txt is highly overall correlated with obesity_cc and 3 other fieldsHigh correlation
obesity_ict is highly overall correlated with waist_circum_preferred and 8 other fieldsHigh correlation
obesity_ict_txt is highly overall correlated with waist_circum_preferred and 8 other fieldsHigh correlation
risk_factors is highly overall correlated with ict and 8 other fieldsHigh correlation

Reproduction

Analysis started2023-08-05 03:31:01.954670
Analysis finished2023-08-05 03:31:20.564687
Duration18.61 seconds
Software versionydata-profiling vv4.2.0
Download configurationconfig.json

Variables

age
Real number (ℝ)

Distinct321
Distinct (%)7.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.764312
Minimum17.5
Maximum79
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size69.2 KiB
2023-08-04T23:31:20.775104image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum17.5
5-th percentile19.8
Q126.4
median37
Q348
95-th percentile60
Maximum79
Range61.5
Interquartile range (IQR)21.6

Descriptive statistics

Standard deviation12.786156
Coefficient of variation (CV)0.33857775
Kurtosis-0.97730004
Mean37.764312
Median Absolute Deviation (MAD)11
Skewness0.27890733
Sum167295.9
Variance163.48578
MonotonicityNot monotonic
2023-08-04T23:31:20.999074image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
41 113
 
2.6%
39 105
 
2.4%
48 104
 
2.3%
40 103
 
2.3%
29 101
 
2.3%
20 100
 
2.3%
38 99
 
2.2%
27 96
 
2.2%
36 96
 
2.2%
35 96
 
2.2%
Other values (311) 3417
77.1%
ValueCountFrequency (%)
17.5 1
 
< 0.1%
18 41
0.9%
18.1 8
 
0.2%
18.2 7
 
0.2%
18.3 4
 
0.1%
18.4 4
 
0.1%
18.5 7
 
0.2%
18.6 5
 
0.1%
18.7 3
 
0.1%
18.8 9
 
0.2%
ValueCountFrequency (%)
79 1
 
< 0.1%
70 1
 
< 0.1%
69 1
 
< 0.1%
68 1
 
< 0.1%
66 4
 
0.1%
65 32
0.7%
64.8 1
 
< 0.1%
64.6 1
 
< 0.1%
64.3 1
 
< 0.1%
64 38
0.9%

age_range
Categorical

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size69.2 KiB
26-35
1068 
36-45
1038 
17-25
1001 
46-55
842 
56-65
473 

Length

Max length6
Median length5
Mean length5.0018059
Min length5

Characters and Unicode

Total characters22158
Distinct characters9
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row46-55
2nd row46-55
3rd row26-35
4th row46-55
5th row46-55

Common Values

ValueCountFrequency (%)
26-35 1068
24.1%
36-45 1038
23.4%
17-25 1001
22.6%
46-55 842
19.0%
56-65 473
10.7%
66-100 8
 
0.2%

Length

2023-08-04T23:31:21.184739image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-04T23:31:21.460763image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
26-35 1068
24.1%
36-45 1038
23.4%
17-25 1001
22.6%
46-55 842
19.0%
56-65 473
10.7%
66-100 8
 
0.2%

Most occurring characters

ValueCountFrequency (%)
5 5737
25.9%
- 4430
20.0%
6 3910
17.6%
3 2106
 
9.5%
2 2069
 
9.3%
4 1880
 
8.5%
1 1009
 
4.6%
7 1001
 
4.5%
0 16
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 17728
80.0%
Dash Punctuation 4430
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 5737
32.4%
6 3910
22.1%
3 2106
 
11.9%
2 2069
 
11.7%
4 1880
 
10.6%
1 1009
 
5.7%
7 1001
 
5.6%
0 16
 
0.1%
Dash Punctuation
ValueCountFrequency (%)
- 4430
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 22158
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
5 5737
25.9%
- 4430
20.0%
6 3910
17.6%
3 2106
 
9.5%
2 2069
 
9.3%
4 1880
 
8.5%
1 1009
 
4.6%
7 1001
 
4.5%
0 16
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 22158
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5 5737
25.9%
- 4430
20.0%
6 3910
17.6%
3 2106
 
9.5%
2 2069
 
9.3%
4 1880
 
8.5%
1 1009
 
4.6%
7 1001
 
4.5%
0 16
 
0.1%

gender
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size69.2 KiB
female
2340 
male
2090 

Length

Max length6
Median length6
Mean length5.0564334
Min length4

Characters and Unicode

Total characters22400
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowmale
2nd rowmale
3rd rowmale
4th rowmale
5th rowmale

Common Values

ValueCountFrequency (%)
female 2340
52.8%
male 2090
47.2%

Length

2023-08-04T23:31:21.643965image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-04T23:31:21.821681image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
female 2340
52.8%
male 2090
47.2%

Most occurring characters

ValueCountFrequency (%)
e 6770
30.2%
m 4430
19.8%
a 4430
19.8%
l 4430
19.8%
f 2340
 
10.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 22400
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 6770
30.2%
m 4430
19.8%
a 4430
19.8%
l 4430
19.8%
f 2340
 
10.4%

Most occurring scripts

ValueCountFrequency (%)
Latin 22400
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 6770
30.2%
m 4430
19.8%
a 4430
19.8%
l 4430
19.8%
f 2340
 
10.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 22400
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 6770
30.2%
m 4430
19.8%
a 4430
19.8%
l 4430
19.8%
f 2340
 
10.4%

height
Real number (ℝ)

Distinct515
Distinct (%)11.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean170.93756
Minimum124.7902
Maximum218.2876
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size69.2 KiB
2023-08-04T23:31:22.364176image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum124.7902
5-th percentile154.9908
Q1163.322
median170.4086
Q3177.9016
95-th percentile188.44768
Maximum218.2876
Range93.4974
Interquartile range (IQR)14.5796

Descriptive statistics

Standard deviation10.387864
Coefficient of variation (CV)0.060769934
Kurtosis-0.00065702733
Mean170.93756
Median Absolute Deviation (MAD)7.2898
Skewness0.24526911
Sum757253.4
Variance107.90773
MonotonicityNot monotonic
2023-08-04T23:31:22.564488image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
164.0078 29
 
0.7%
171.5008 27
 
0.6%
168.3004 25
 
0.6%
168.5036 25
 
0.6%
164.4904 25
 
0.6%
172.8978 24
 
0.5%
176.5046 24
 
0.5%
162.0012 22
 
0.5%
166.497 22
 
0.5%
171.0944 22
 
0.5%
Other values (505) 4185
94.5%
ValueCountFrequency (%)
124.7902 1
< 0.1%
131.4958 1
< 0.1%
138.0998 1
< 0.1%
138.2014 1
< 0.1%
142.5956 1
< 0.1%
143.6116 1
< 0.1%
144.5006 1
< 0.1%
145.288 1
< 0.1%
145.796 1
< 0.1%
146.5072 1
< 0.1%
ValueCountFrequency (%)
218.2876 1
< 0.1%
208.407 1
< 0.1%
207.2894 1
< 0.1%
206.8068 1
< 0.1%
205.4098 1
< 0.1%
205.3082 1
< 0.1%
204.597 1
< 0.1%
203.9112 1
< 0.1%
203.3016 1
< 0.1%
202.692 1
< 0.1%

weight
Real number (ℝ)

Distinct993
Distinct (%)22.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean75.205328
Minimum37.33242
Maximum181.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size69.2 KiB
2023-08-04T23:31:22.760873image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum37.33242
5-th percentile51.302
Q161.95738
median72.186
Q384.898
95-th percentile109.187
Maximum181.6
Range144.26758
Interquartile range (IQR)22.94062

Descriptive statistics

Standard deviation18.419132
Coefficient of variation (CV)0.24491791
Kurtosis2.0663078
Mean75.205328
Median Absolute Deviation (MAD)11.16159
Skewness1.1269469
Sum333159.6
Variance339.26442
MonotonicityNot monotonic
2023-08-04T23:31:22.959274image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
68.554 35
 
0.8%
73.548 26
 
0.6%
75.364 25
 
0.6%
58.339 25
 
0.6%
78.769 23
 
0.5%
76.953 22
 
0.5%
71.959 22
 
0.5%
65.149 20
 
0.5%
56.75 20
 
0.5%
66.965 20
 
0.5%
Other values (983) 4192
94.6%
ValueCountFrequency (%)
37.33242 1
< 0.1%
37.93624 1
< 0.1%
39.271 1
< 0.1%
39.952 1
< 0.1%
41.53646 1
< 0.1%
41.541 1
< 0.1%
41.93598 1
< 0.1%
42.222 1
< 0.1%
42.93932 1
< 0.1%
43.33884 1
< 0.1%
ValueCountFrequency (%)
181.6 1
 
< 0.1%
160.035 1
 
< 0.1%
156.857 1
 
< 0.1%
156.63 3
0.1%
156.176 2
< 0.1%
155.949 1
 
< 0.1%
155.041 3
0.1%
154.814 1
 
< 0.1%
154.587 1
 
< 0.1%
154.133 1
 
< 0.1%

waist_circum_preferred
Real number (ℝ)

Distinct631
Distinct (%)14.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean84.701265
Minimum55.7022
Maximum170.2054
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size69.2 KiB
2023-08-04T23:31:23.169144image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum55.7022
5-th percentile66.4972
Q175.0062
median83.0072
Q392.2782
95-th percentile108.8898
Maximum170.2054
Range114.5032
Interquartile range (IQR)17.272

Descriptive statistics

Standard deviation13.362246
Coefficient of variation (CV)0.15775734
Kurtosis1.9160554
Mean84.701265
Median Absolute Deviation (MAD)8.4836
Skewness0.97778939
Sum375226.61
Variance178.54963
MonotonicityNot monotonic
2023-08-04T23:31:23.394135image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
78.994 28
 
0.6%
74.7014 25
 
0.6%
80.4926 24
 
0.5%
73.9902 24
 
0.5%
80.01 23
 
0.5%
72.4916 23
 
0.5%
79.502 23
 
0.5%
75.0062 23
 
0.5%
72.009 22
 
0.5%
73.5076 22
 
0.5%
Other values (621) 4193
94.7%
ValueCountFrequency (%)
55.7022 1
< 0.1%
56.3118 1
< 0.1%
56.6928 1
< 0.1%
56.9976 2
< 0.1%
57.2008 1
< 0.1%
57.404 1
< 0.1%
57.8104 1
< 0.1%
57.9882 1
< 0.1%
58.0898 1
< 0.1%
58.3946 1
< 0.1%
ValueCountFrequency (%)
170.2054 1
< 0.1%
165.3032 1
< 0.1%
152.908 1
< 0.1%
149.8092 1
< 0.1%
148.4122 1
< 0.1%
142.1892 1
< 0.1%
141.9098 1
< 0.1%
140.9954 2
< 0.1%
140.6906 1
< 0.1%
139.7 1
< 0.1%

hip_circum
Real number (ℝ)

Distinct521
Distinct (%)11.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean103.51346
Minimum80.01
Maximum183.388
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size69.2 KiB
2023-08-04T23:31:23.605320image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum80.01
5-th percentile90.805
Q196.6978
median101.9048
Q3108.1024
95-th percentile121.64568
Maximum183.388
Range103.378
Interquartile range (IQR)11.4046

Descriptive statistics

Standard deviation10.273928
Coefficient of variation (CV)0.099252101
Kurtosis5.4614036
Mean103.51346
Median Absolute Deviation (MAD)5.588
Skewness1.6151061
Sum458564.62
Variance105.5536
MonotonicityNot monotonic
2023-08-04T23:31:23.812344image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
98.5012 37
 
0.8%
96.012 37
 
0.8%
103.505 37
 
0.8%
97.9932 37
 
0.8%
105.2068 35
 
0.8%
99.0092 34
 
0.8%
95.504 32
 
0.7%
99.9998 32
 
0.7%
101.4984 32
 
0.7%
106.5022 31
 
0.7%
Other values (511) 4086
92.2%
ValueCountFrequency (%)
80.01 1
< 0.1%
81.0006 1
< 0.1%
81.1022 1
< 0.1%
81.3054 1
< 0.1%
81.6102 1
< 0.1%
81.7118 1
< 0.1%
81.9912 1
< 0.1%
82.0928 1
< 0.1%
82.1944 1
< 0.1%
82.4992 1
< 0.1%
ValueCountFrequency (%)
183.388 1
< 0.1%
173.8122 1
< 0.1%
168.7068 1
< 0.1%
167.2082 1
< 0.1%
165.989 1
< 0.1%
164.4904 1
< 0.1%
161.5948 1
< 0.1%
161.0106 1
< 0.1%
160.401 1
< 0.1%
157.7086 1
< 0.1%

gender_bin
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size69.2 KiB
0
2340 
1
2090 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4430
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
0 2340
52.8%
1 2090
47.2%

Length

2023-08-04T23:31:23.989327image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-04T23:31:24.146820image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
0 2340
52.8%
1 2090
47.2%

Most occurring characters

ValueCountFrequency (%)
0 2340
52.8%
1 2090
47.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4430
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2340
52.8%
1 2090
47.2%

Most occurring scripts

ValueCountFrequency (%)
Common 4430
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2340
52.8%
1 2090
47.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4430
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2340
52.8%
1 2090
47.2%

bmi
Real number (ℝ)

Distinct4360
Distinct (%)98.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25.608337
Minimum15.26363
Maximum57.18016
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size69.2 KiB
2023-08-04T23:31:24.307942image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum15.26363
5-th percentile19.303918
Q121.940406
median24.498035
Q327.973753
95-th percentile35.943392
Maximum57.18016
Range41.91653
Interquartile range (IQR)6.0333469

Descriptive statistics

Standard deviation5.2884329
Coefficient of variation (CV)0.20651215
Kurtosis3.6002756
Mean25.608337
Median Absolute Deviation (MAD)2.8678445
Skewness1.5065727
Sum113444.93
Variance27.967522
MonotonicityNot monotonic
2023-08-04T23:31:24.489359image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
26.78076826 3
 
0.1%
25.00608087 2
 
< 0.1%
32.17321301 2
 
< 0.1%
20.56090456 2
 
< 0.1%
23.6381695 2
 
< 0.1%
24.46408157 2
 
< 0.1%
23.45569078 2
 
< 0.1%
20.57467771 2
 
< 0.1%
24.07569357 2
 
< 0.1%
25.81653715 2
 
< 0.1%
Other values (4350) 4409
99.5%
ValueCountFrequency (%)
15.26362956 1
< 0.1%
15.9883282 1
< 0.1%
16.13788922 1
< 0.1%
16.53929152 1
< 0.1%
16.57057588 1
< 0.1%
16.61050776 1
< 0.1%
16.64925105 1
< 0.1%
16.66207008 1
< 0.1%
16.72722699 1
< 0.1%
16.8282237 1
< 0.1%
ValueCountFrequency (%)
57.18015963 1
< 0.1%
55.20975085 1
< 0.1%
55.1216726 1
< 0.1%
54.95947618 1
< 0.1%
54.23303619 1
< 0.1%
54.17664219 1
< 0.1%
54.12704563 1
< 0.1%
52.88574683 1
< 0.1%
52.49600496 1
< 0.1%
52.08920258 1
< 0.1%

rcc
Real number (ℝ)

Distinct4308
Distinct (%)97.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.81692117
Minimum0.58329498
Maximum1.153405
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size69.2 KiB
2023-08-04T23:31:24.681658image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0.58329498
5-th percentile0.67922288
Q10.75261413
median0.8209291
Q30.8763767
95-th percentile0.95800983
Maximum1.153405
Range0.57011003
Interquartile range (IQR)0.12376257

Descriptive statistics

Standard deviation0.085733377
Coefficient of variation (CV)0.10494694
Kurtosis-0.38971337
Mean0.81692117
Median Absolute Deviation (MAD)0.061899148
Skewness0.10258363
Sum3618.9608
Variance0.0073502119
MonotonicityNot monotonic
2023-08-04T23:31:24.873510image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 3
 
0.1%
0.81000762 3
 
0.1%
0.8333333333 3
 
0.1%
0.8234175496 2
 
< 0.1%
0.6887033171 2
 
< 0.1%
0.7923098512 2
 
< 0.1%
0.8736416477 2
 
< 0.1%
0.7825506937 2
 
< 0.1%
0.8287959585 2
 
< 0.1%
0.7840995682 2
 
< 0.1%
Other values (4298) 4407
99.5%
ValueCountFrequency (%)
0.5832949839 1
< 0.1%
0.5900981267 1
< 0.1%
0.6033934252 1
< 0.1%
0.605026455 1
< 0.1%
0.6050292101 1
< 0.1%
0.6102551757 1
< 0.1%
0.6109082611 1
< 0.1%
0.6114635747 1
< 0.1%
0.6120020693 1
< 0.1%
0.6183319221 1
< 0.1%
ValueCountFrequency (%)
1.153405018 1
< 0.1%
1.130742765 1
< 0.1%
1.102542192 1
< 0.1%
1.094704922 1
< 0.1%
1.085543608 1
< 0.1%
1.083585565 1
< 0.1%
1.078136201 1
< 0.1%
1.072791658 1
< 0.1%
1.071706368 1
< 0.1%
1.066723879 1
< 0.1%

ict
Real number (ℝ)

Distinct4347
Distinct (%)98.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.49540434
Minimum0.33421014
Maximum0.97087801
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size69.2 KiB
2023-08-04T23:31:25.076934image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0.33421014
5-th percentile0.40400577
Q10.44582916
median0.48235249
Q30.53047123
95-th percentile0.63278092
Maximum0.97087801
Range0.63666786
Interquartile range (IQR)0.084642072

Descriptive statistics

Standard deviation0.071841971
Coefficient of variation (CV)0.14501684
Kurtosis2.8448771
Mean0.49540434
Median Absolute Deviation (MAD)0.040972949
Skewness1.2811297
Sum2194.6412
Variance0.0051612688
MonotonicityNot monotonic
2023-08-04T23:31:25.271860image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.5 3
 
0.1%
0.4414470607 3
 
0.1%
0.5076018359 2
 
< 0.1%
0.5743694615 2
 
< 0.1%
0.3598460552 2
 
< 0.1%
0.4585720923 2
 
< 0.1%
0.5046684602 2
 
< 0.1%
0.4383907363 2
 
< 0.1%
0.4939657592 2
 
< 0.1%
0.5348804302 2
 
< 0.1%
Other values (4337) 4408
99.5%
ValueCountFrequency (%)
0.3342101418 1
< 0.1%
0.3429639212 1
< 0.1%
0.3510688836 1
< 0.1%
0.3526946973 1
< 0.1%
0.3565334531 1
< 0.1%
0.3586460633 1
< 0.1%
0.3598460552 2
< 0.1%
0.3643308359 1
< 0.1%
0.3650768748 1
< 0.1%
0.3654984069 1
< 0.1%
ValueCountFrequency (%)
0.9708780064 1
< 0.1%
0.9177831053 1
< 0.1%
0.8755938242 1
< 0.1%
0.8732230925 1
< 0.1%
0.8648608644 1
< 0.1%
0.8627543777 1
< 0.1%
0.8555067462 1
< 0.1%
0.8469601677 1
< 0.1%
0.8352392416 1
< 0.1%
0.8342395526 1
< 0.1%

obesity_bmi
Categorical

Distinct4
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size69.2 KiB
1
2313 
2
1311 
3
716 
0
 
90

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4430
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row3
3rd row2
4th row3
5th row3

Common Values

ValueCountFrequency (%)
1 2313
52.2%
2 1311
29.6%
3 716
 
16.2%
0 90
 
2.0%

Length

2023-08-04T23:31:25.453816image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-04T23:31:25.625682image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
1 2313
52.2%
2 1311
29.6%
3 716
 
16.2%
0 90
 
2.0%

Most occurring characters

ValueCountFrequency (%)
1 2313
52.2%
2 1311
29.6%
3 716
 
16.2%
0 90
 
2.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4430
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 2313
52.2%
2 1311
29.6%
3 716
 
16.2%
0 90
 
2.0%

Most occurring scripts

ValueCountFrequency (%)
Common 4430
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 2313
52.2%
2 1311
29.6%
3 716
 
16.2%
0 90
 
2.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4430
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 2313
52.2%
2 1311
29.6%
3 716
 
16.2%
0 90
 
2.0%

obesity_bmi_txt
Categorical

Distinct4
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size69.2 KiB
1-Normal
2313 
2-Sobrepeso
1311 
3-Obesidad
716 
0-Bajo Peso
 
90

Length

Max length11
Median length8
Mean length9.272009
Min length8

Characters and Unicode

Total characters41075
Distinct characters23
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3-Obesidad
2nd row3-Obesidad
3rd row2-Sobrepeso
4th row3-Obesidad
5th row3-Obesidad

Common Values

ValueCountFrequency (%)
1-Normal 2313
52.2%
2-Sobrepeso 1311
29.6%
3-Obesidad 716
 
16.2%
0-Bajo Peso 90
 
2.0%

Length

2023-08-04T23:31:25.794469image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-04T23:31:25.978129image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
1-normal 2313
51.2%
2-sobrepeso 1311
29.0%
3-obesidad 716
 
15.8%
0-bajo 90
 
2.0%
peso 90
 
2.0%

Most occurring characters

ValueCountFrequency (%)
o 5115
12.5%
- 4430
10.8%
r 3624
 
8.8%
e 3428
 
8.3%
a 3119
 
7.6%
l 2313
 
5.6%
1 2313
 
5.6%
m 2313
 
5.6%
N 2313
 
5.6%
s 2117
 
5.2%
Other values (13) 9990
24.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 27605
67.2%
Uppercase Letter 4520
 
11.0%
Dash Punctuation 4430
 
10.8%
Decimal Number 4430
 
10.8%
Space Separator 90
 
0.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 5115
18.5%
r 3624
13.1%
e 3428
12.4%
a 3119
11.3%
l 2313
8.4%
m 2313
8.4%
s 2117
7.7%
b 2027
 
7.3%
d 1432
 
5.2%
p 1311
 
4.7%
Other values (2) 806
 
2.9%
Uppercase Letter
ValueCountFrequency (%)
N 2313
51.2%
S 1311
29.0%
O 716
 
15.8%
B 90
 
2.0%
P 90
 
2.0%
Decimal Number
ValueCountFrequency (%)
1 2313
52.2%
2 1311
29.6%
3 716
 
16.2%
0 90
 
2.0%
Dash Punctuation
ValueCountFrequency (%)
- 4430
100.0%
Space Separator
ValueCountFrequency (%)
90
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 32125
78.2%
Common 8950
 
21.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 5115
15.9%
r 3624
11.3%
e 3428
10.7%
a 3119
9.7%
l 2313
7.2%
m 2313
7.2%
N 2313
7.2%
s 2117
6.6%
b 2027
 
6.3%
d 1432
 
4.5%
Other values (7) 4324
13.5%
Common
ValueCountFrequency (%)
- 4430
49.5%
1 2313
25.8%
2 1311
 
14.6%
3 716
 
8.0%
0 90
 
1.0%
90
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 41075
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 5115
12.5%
- 4430
10.8%
r 3624
 
8.8%
e 3428
 
8.3%
a 3119
 
7.6%
l 2313
 
5.6%
1 2313
 
5.6%
m 2313
 
5.6%
N 2313
 
5.6%
s 2117
 
5.2%
Other values (13) 9990
24.3%

obesity_cc
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size69.2 KiB
0
2898 
1
1532 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4430
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
0 2898
65.4%
1 1532
34.6%

Length

2023-08-04T23:31:26.143774image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-04T23:31:26.312464image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
0 2898
65.4%
1 1532
34.6%

Most occurring characters

ValueCountFrequency (%)
0 2898
65.4%
1 1532
34.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4430
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2898
65.4%
1 1532
34.6%

Most occurring scripts

ValueCountFrequency (%)
Common 4430
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2898
65.4%
1 1532
34.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4430
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2898
65.4%
1 1532
34.6%

obesity_cc_txt
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size69.2 KiB
0-Bajo
2898 
1-Alto
1532 

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters26580
Distinct characters10
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1-Alto
2nd row1-Alto
3rd row1-Alto
4th row1-Alto
5th row1-Alto

Common Values

ValueCountFrequency (%)
0-Bajo 2898
65.4%
1-Alto 1532
34.6%

Length

2023-08-04T23:31:26.450753image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-04T23:31:26.608778image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
0-bajo 2898
65.4%
1-alto 1532
34.6%

Most occurring characters

ValueCountFrequency (%)
- 4430
16.7%
o 4430
16.7%
0 2898
10.9%
B 2898
10.9%
a 2898
10.9%
j 2898
10.9%
1 1532
 
5.8%
A 1532
 
5.8%
l 1532
 
5.8%
t 1532
 
5.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 13290
50.0%
Dash Punctuation 4430
 
16.7%
Decimal Number 4430
 
16.7%
Uppercase Letter 4430
 
16.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 4430
33.3%
a 2898
21.8%
j 2898
21.8%
l 1532
 
11.5%
t 1532
 
11.5%
Decimal Number
ValueCountFrequency (%)
0 2898
65.4%
1 1532
34.6%
Uppercase Letter
ValueCountFrequency (%)
B 2898
65.4%
A 1532
34.6%
Dash Punctuation
ValueCountFrequency (%)
- 4430
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 17720
66.7%
Common 8860
33.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 4430
25.0%
B 2898
16.4%
a 2898
16.4%
j 2898
16.4%
A 1532
 
8.6%
l 1532
 
8.6%
t 1532
 
8.6%
Common
ValueCountFrequency (%)
- 4430
50.0%
0 2898
32.7%
1 1532
 
17.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 26580
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 4430
16.7%
o 4430
16.7%
0 2898
10.9%
B 2898
10.9%
a 2898
10.9%
j 2898
10.9%
1 1532
 
5.8%
A 1532
 
5.8%
l 1532
 
5.8%
t 1532
 
5.8%

obesity_rcc
Categorical

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size69.2 KiB
0
3556 
1
543 
2
 
331

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4430
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 3556
80.3%
1 543
 
12.3%
2 331
 
7.5%

Length

2023-08-04T23:31:26.749043image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-04T23:31:26.911138image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
0 3556
80.3%
1 543
 
12.3%
2 331
 
7.5%

Most occurring characters

ValueCountFrequency (%)
0 3556
80.3%
1 543
 
12.3%
2 331
 
7.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4430
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 3556
80.3%
1 543
 
12.3%
2 331
 
7.5%

Most occurring scripts

ValueCountFrequency (%)
Common 4430
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 3556
80.3%
1 543
 
12.3%
2 331
 
7.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4430
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 3556
80.3%
1 543
 
12.3%
2 331
 
7.5%

obesity_rcc_txt
Categorical

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size69.2 KiB
0-Bajo
3556 
1-Medio
543 
2-Alto
 
331

Length

Max length7
Median length6
Mean length6.1225734
Min length6

Characters and Unicode

Total characters27123
Distinct characters15
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1-Medio
2nd row0-Bajo
3rd row0-Bajo
4th row1-Medio
5th row0-Bajo

Common Values

ValueCountFrequency (%)
0-Bajo 3556
80.3%
1-Medio 543
 
12.3%
2-Alto 331
 
7.5%

Length

2023-08-04T23:31:27.060372image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-04T23:31:27.231246image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
0-bajo 3556
80.3%
1-medio 543
 
12.3%
2-alto 331
 
7.5%

Most occurring characters

ValueCountFrequency (%)
- 4430
16.3%
o 4430
16.3%
0 3556
13.1%
B 3556
13.1%
a 3556
13.1%
j 3556
13.1%
1 543
 
2.0%
M 543
 
2.0%
e 543
 
2.0%
d 543
 
2.0%
Other values (5) 1867
6.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 13833
51.0%
Dash Punctuation 4430
 
16.3%
Decimal Number 4430
 
16.3%
Uppercase Letter 4430
 
16.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 4430
32.0%
a 3556
25.7%
j 3556
25.7%
e 543
 
3.9%
d 543
 
3.9%
i 543
 
3.9%
l 331
 
2.4%
t 331
 
2.4%
Decimal Number
ValueCountFrequency (%)
0 3556
80.3%
1 543
 
12.3%
2 331
 
7.5%
Uppercase Letter
ValueCountFrequency (%)
B 3556
80.3%
M 543
 
12.3%
A 331
 
7.5%
Dash Punctuation
ValueCountFrequency (%)
- 4430
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 18263
67.3%
Common 8860
32.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 4430
24.3%
B 3556
19.5%
a 3556
19.5%
j 3556
19.5%
M 543
 
3.0%
e 543
 
3.0%
d 543
 
3.0%
i 543
 
3.0%
A 331
 
1.8%
l 331
 
1.8%
Common
ValueCountFrequency (%)
- 4430
50.0%
0 3556
40.1%
1 543
 
6.1%
2 331
 
3.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 27123
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 4430
16.3%
o 4430
16.3%
0 3556
13.1%
B 3556
13.1%
a 3556
13.1%
j 3556
13.1%
1 543
 
2.0%
M 543
 
2.0%
e 543
 
2.0%
d 543
 
2.0%
Other values (5) 1867
6.9%

obesity_ict
Categorical

Distinct4
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size69.2 KiB
1
2399 
2
1268 
3
428 
0
335 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4430
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row2
3rd row2
4th row2
5th row2

Common Values

ValueCountFrequency (%)
1 2399
54.2%
2 1268
28.6%
3 428
 
9.7%
0 335
 
7.6%

Length

2023-08-04T23:31:27.378965image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-04T23:31:27.547030image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
1 2399
54.2%
2 1268
28.6%
3 428
 
9.7%
0 335
 
7.6%

Most occurring characters

ValueCountFrequency (%)
1 2399
54.2%
2 1268
28.6%
3 428
 
9.7%
0 335
 
7.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4430
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 2399
54.2%
2 1268
28.6%
3 428
 
9.7%
0 335
 
7.6%

Most occurring scripts

ValueCountFrequency (%)
Common 4430
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 2399
54.2%
2 1268
28.6%
3 428
 
9.7%
0 335
 
7.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4430
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 2399
54.2%
2 1268
28.6%
3 428
 
9.7%
0 335
 
7.6%

obesity_ict_txt
Categorical

Distinct4
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size69.2 KiB
1-Normal
2399 
2-Sobrepeso
1268 
3-Obesidad
428 
0-Delgado
335 

Length

Max length11
Median length8
Mean length9.1275395
Min length8

Characters and Unicode

Total characters40435
Distinct characters21
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3-Obesidad
2nd row2-Sobrepeso
3rd row2-Sobrepeso
4th row2-Sobrepeso
5th row2-Sobrepeso

Common Values

ValueCountFrequency (%)
1-Normal 2399
54.2%
2-Sobrepeso 1268
28.6%
3-Obesidad 428
 
9.7%
0-Delgado 335
 
7.6%

Length

2023-08-04T23:31:27.710924image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-04T23:31:27.892606image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
1-normal 2399
54.2%
2-sobrepeso 1268
28.6%
3-obesidad 428
 
9.7%
0-delgado 335
 
7.6%

Most occurring characters

ValueCountFrequency (%)
o 5270
13.0%
- 4430
11.0%
r 3667
9.1%
e 3299
 
8.2%
a 3162
 
7.8%
l 2734
 
6.8%
1 2399
 
5.9%
N 2399
 
5.9%
m 2399
 
5.9%
s 1696
 
4.2%
Other values (11) 8980
22.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 27145
67.1%
Dash Punctuation 4430
 
11.0%
Decimal Number 4430
 
11.0%
Uppercase Letter 4430
 
11.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 5270
19.4%
r 3667
13.5%
e 3299
12.2%
a 3162
11.6%
l 2734
10.1%
m 2399
8.8%
s 1696
 
6.2%
b 1696
 
6.2%
p 1268
 
4.7%
d 1191
 
4.4%
Other values (2) 763
 
2.8%
Decimal Number
ValueCountFrequency (%)
1 2399
54.2%
2 1268
28.6%
3 428
 
9.7%
0 335
 
7.6%
Uppercase Letter
ValueCountFrequency (%)
N 2399
54.2%
S 1268
28.6%
O 428
 
9.7%
D 335
 
7.6%
Dash Punctuation
ValueCountFrequency (%)
- 4430
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 31575
78.1%
Common 8860
 
21.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 5270
16.7%
r 3667
11.6%
e 3299
10.4%
a 3162
10.0%
l 2734
8.7%
N 2399
7.6%
m 2399
7.6%
s 1696
 
5.4%
b 1696
 
5.4%
S 1268
 
4.0%
Other values (6) 3985
12.6%
Common
ValueCountFrequency (%)
- 4430
50.0%
1 2399
27.1%
2 1268
 
14.3%
3 428
 
4.8%
0 335
 
3.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 40435
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 5270
13.0%
- 4430
11.0%
r 3667
9.1%
e 3299
 
8.2%
a 3162
 
7.8%
l 2734
 
6.8%
1 2399
 
5.9%
N 2399
 
5.9%
m 2399
 
5.9%
s 1696
 
4.2%
Other values (11) 8980
22.2%

risk_factors
Categorical

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size69.2 KiB
0
2020 
3
710 
1
667 
4
633 
2
400 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4430
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4
2nd row3
3rd row3
4th row4
5th row3

Common Values

ValueCountFrequency (%)
0 2020
45.6%
3 710
 
16.0%
1 667
 
15.1%
4 633
 
14.3%
2 400
 
9.0%

Length

2023-08-04T23:31:28.043975image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-04T23:31:28.213868image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
0 2020
45.6%
3 710
 
16.0%
1 667
 
15.1%
4 633
 
14.3%
2 400
 
9.0%

Most occurring characters

ValueCountFrequency (%)
0 2020
45.6%
3 710
 
16.0%
1 667
 
15.1%
4 633
 
14.3%
2 400
 
9.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4430
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2020
45.6%
3 710
 
16.0%
1 667
 
15.1%
4 633
 
14.3%
2 400
 
9.0%

Most occurring scripts

ValueCountFrequency (%)
Common 4430
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2020
45.6%
3 710
 
16.0%
1 667
 
15.1%
4 633
 
14.3%
2 400
 
9.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4430
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2020
45.6%
3 710
 
16.0%
1 667
 
15.1%
4 633
 
14.3%
2 400
 
9.0%

Interactions

2023-08-04T23:31:18.097894image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-08-04T23:31:06.545905image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-08-04T23:31:08.164984image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-08-04T23:31:09.731142image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-08-04T23:31:11.400755image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-08-04T23:31:13.307925image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-08-04T23:31:15.000191image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-08-04T23:31:16.535228image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-08-04T23:31:18.299340image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-08-04T23:31:06.760997image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-08-04T23:31:08.364741image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-08-04T23:31:09.937568image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-08-04T23:31:11.612339image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-08-04T23:31:13.513159image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-08-04T23:31:15.211974image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-08-04T23:31:16.734200image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-08-04T23:31:18.495052image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-08-04T23:31:06.971155image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-08-04T23:31:08.547948image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-08-04T23:31:10.137665image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-08-04T23:31:11.811514image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-08-04T23:31:13.714149image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-08-04T23:31:15.388673image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-08-04T23:31:16.921054image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-08-04T23:31:18.705093image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-08-04T23:31:07.178807image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-08-04T23:31:08.746858image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-08-04T23:31:10.354996image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-08-04T23:31:12.020164image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-08-04T23:31:13.961756image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-08-04T23:31:15.585449image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-08-04T23:31:17.125455image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-08-04T23:31:18.916260image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-08-04T23:31:07.375679image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-08-04T23:31:08.957799image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-08-04T23:31:10.561936image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-08-04T23:31:12.216217image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-08-04T23:31:14.157504image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-08-04T23:31:15.774284image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-08-04T23:31:17.325417image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-08-04T23:31:19.118616image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-08-04T23:31:07.576334image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-08-04T23:31:09.155525image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-08-04T23:31:10.763202image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-08-04T23:31:12.409802image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-08-04T23:31:14.357365image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-08-04T23:31:15.968220image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-08-04T23:31:17.517428image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-08-04T23:31:19.304998image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-08-04T23:31:07.766935image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-08-04T23:31:09.338938image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-08-04T23:31:10.989336image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-08-04T23:31:12.596892image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-08-04T23:31:14.546425image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-08-04T23:31:16.139247image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-08-04T23:31:17.696486image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-08-04T23:31:19.505917image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-08-04T23:31:07.964316image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-08-04T23:31:09.530276image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-08-04T23:31:11.186501image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-08-04T23:31:12.793207image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-08-04T23:31:14.757889image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-08-04T23:31:16.335390image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-08-04T23:31:17.898815image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Correlations

2023-08-04T23:31:28.387555image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ageheightweightwaist_circum_preferredhip_circumbmirccictage_rangegendergender_binobesity_bmiobesity_bmi_txtobesity_ccobesity_cc_txtobesity_rccobesity_rcc_txtobesity_ictobesity_ict_txtrisk_factors
age1.000-0.0740.2470.2970.2230.3650.2380.3660.8530.0330.0330.1900.1900.2850.2850.1840.1840.1960.1960.188
height-0.0741.0000.5850.4490.2220.0990.4730.0670.0380.6410.6410.0570.0570.1060.1060.0790.0790.0900.0900.076
weight0.2470.5851.0000.9030.7800.8400.6100.7300.1130.4650.4650.5330.5330.5860.5860.1880.1880.3890.3890.340
waist_circum_preferred0.2970.4490.9031.0000.6730.8130.8070.9050.1400.4710.4710.5240.5240.6950.6950.3490.3490.5770.5770.424
hip_circum0.2230.2220.7800.6731.0000.8140.1570.6420.0980.1090.1090.5360.5360.6300.6300.1900.1900.4590.4590.350
bmi0.3650.0990.8400.8130.8141.0000.4580.8730.1540.2240.2240.7300.7300.6780.6780.2950.2950.5890.5890.456
rcc0.2380.4730.6100.8070.1570.4581.0000.6950.1640.6810.6810.2750.2750.4520.4520.4310.4310.3750.3750.319
ict0.3660.0670.7300.9050.6420.8730.6951.0000.1610.2690.2690.5820.5820.7390.7390.4310.4310.7390.7390.514
age_range0.8530.0380.1130.1400.0980.1540.1640.1611.0000.0000.0000.1900.1900.2780.2780.1720.1720.1920.1920.180
gender0.0330.6410.4650.4710.1090.2240.6810.2690.0001.0001.0000.1920.1920.0870.0870.2140.2140.2250.2250.188
gender_bin0.0330.6410.4650.4710.1090.2240.6810.2690.0001.0001.0000.1920.1920.0870.0870.2140.2140.2250.2250.188
obesity_bmi0.1900.0570.5330.5240.5360.7300.2750.5820.1900.1920.1921.0001.0000.6620.6620.2710.2710.5170.5170.562
obesity_bmi_txt0.1900.0570.5330.5240.5360.7300.2750.5820.1900.1920.1921.0001.0000.6620.6620.2710.2710.5170.5170.562
obesity_cc0.2850.1060.5860.6950.6300.6780.4520.7390.2780.0870.0870.6620.6621.0001.0000.5470.5470.7810.7810.920
obesity_cc_txt0.2850.1060.5860.6950.6300.6780.4520.7390.2780.0870.0870.6620.6621.0001.0000.5470.5470.7810.7810.920
obesity_rcc0.1840.0790.1880.3490.1900.2950.4310.4310.1720.2140.2140.2710.2710.5470.5471.0001.0000.4660.4660.598
obesity_rcc_txt0.1840.0790.1880.3490.1900.2950.4310.4310.1720.2140.2140.2710.2710.5470.5471.0001.0000.4660.4660.598
obesity_ict0.1960.0900.3890.5770.4590.5890.3750.7390.1920.2250.2250.5170.5170.7810.7810.4660.4661.0001.0000.618
obesity_ict_txt0.1960.0900.3890.5770.4590.5890.3750.7390.1920.2250.2250.5170.5170.7810.7810.4660.4661.0001.0000.618
risk_factors0.1880.0760.3400.4240.3500.4560.3190.5140.1800.1880.1880.5620.5620.9200.9200.5980.5980.6180.6181.000

Missing values

2023-08-04T23:31:19.882804image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
A simple visualization of nullity by column.
2023-08-04T23:31:20.332199image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

ageage_rangegenderheightweightwaist_circum_preferredhip_circumgender_binbmirccictobesity_bmiobesity_bmi_txtobesity_ccobesity_cc_txtobesity_rccobesity_rcc_txtobesity_ictobesity_ict_txtrisk_factors
147.046-55male187.1980156.630138.5062142.1892144.6963990.9740980.73989133-Obesidad11-Alto11-Medio33-Obesidad4
250.046-55male180.594098.06498.3996109.9058130.0678910.8953090.54486633-Obesidad11-Alto00-Bajo22-Sobrepeso3
328.026-35male182.702289.21197.0026107.0102126.7258090.9064800.53093322-Sobrepeso11-Alto00-Bajo22-Sobrepeso3
452.046-55male185.9026113.273105.9942111.0996132.7759640.9540470.57016033-Obesidad11-Alto11-Medio22-Sobrepeso4
550.046-55male183.1086108.279104.0892112.3950132.2943650.9261020.56845633-Obesidad11-Alto00-Bajo22-Sobrepeso3
627.026-35female165.100057.88574.9046100.9904021.2359600.7417000.45369211-Normal00-Bajo00-Bajo11-Normal0
741.036-45female166.497072.18678.2066104.5972026.0399500.7476930.46971822-Sobrepeso00-Bajo00-Bajo11-Normal1
842.036-45male174.498083.53687.1982100.4062127.4342300.8684540.49970922-Sobrepeso00-Bajo00-Bajo11-Normal1
948.046-55female163.093466.51170.0024105.0036025.0046460.6666670.42921722-Sobrepeso00-Bajo00-Bajo11-Normal1
1042.036-45female169.494270.59769.3928105.5878024.5740400.6572050.40941111-Normal00-Bajo00-Bajo00-Delgado0
ageage_rangegenderheightweightwaist_circum_preferredhip_circumgender_binbmirccictobesity_bmiobesity_bmi_txtobesity_ccobesity_cc_txtobesity_rccobesity_rcc_txtobesity_ictobesity_ict_txtrisk_factors
445543.036-45male176.199888.98493.3958104.5972128.6616440.8929090.53005622-Sobrepeso00-Bajo00-Bajo22-Sobrepeso2
445640.036-45female163.398256.52368.097495.8088021.1704780.7107640.41675711-Normal00-Bajo00-Bajo11-Normal0
445749.046-55female158.191269.91681.7118110.3122027.9390690.7407320.51653822-Sobrepeso11-Alto00-Bajo22-Sobrepeso3
445850.046-55female154.609859.70171.6026101.9048024.9751190.7026420.46311811-Normal00-Bajo00-Bajo11-Normal0
445929.026-35female161.010679.45087.9094112.9030030.6467880.7786280.54598533-Obesidad11-Alto00-Bajo22-Sobrepeso3
446035.026-35female152.806449.25961.595093.0910021.0961160.6616640.40309211-Normal00-Bajo00-Bajo00-Delgado0
446140.036-45female168.605267.64673.8124100.4062023.7957940.7351380.43778211-Normal00-Bajo00-Bajo11-Normal0
446223.017-25female168.503673.77578.0034109.7026025.9830930.7110440.46291822-Sobrepeso00-Bajo00-Bajo11-Normal1
446324.017-25male170.510265.14975.793697.7900122.4082030.7750650.44451111-Normal00-Bajo00-Bajo11-Normal0
446422.017-25female170.408661.29068.8086101.6000021.1060330.6772500.40378611-Normal00-Bajo00-Bajo00-Delgado0

Duplicate rows

Most frequently occurring

ageage_rangegenderheightweightwaist_circum_preferredhip_circumgender_binbmirccictobesity_bmiobesity_bmi_txtobesity_ccobesity_cc_txtobesity_rccobesity_rcc_txtobesity_ictobesity_ict_txtrisk_factors# duplicates
032.026-35male177.088885.57989.8906108.1024127.2888360.8315320.50760222-Sobrepeso00-Bajo00-Bajo11-Normal12